1. Field
One or more embodiments of the present invention relate to a method and an apparatus for detecting an object, and more particularly, to a method and an apparatus for detecting an object, particularly a face, using a multi-stage classifier.
2. Description of the Related Art
As society develops into an advanced information society, there is increasing consumer demand for goods and services in fields such as information security and customer management. Accordingly, the present inventors have determined that a reliable system for identifying a particular manager and for securing visitors is required.
Recently, face recognition methodology has received much publicity. Face recognition methods are widely used in not only the above-mentioned types of security systems but also in face detection fields such as inspection, customer management in a large mall, individual personal protection, unmanned vending machines, etc., and in various other fields that use a computer's information processing ability and a wide area communication network including the Internet.
Face detection technology is one of the basic technologies applied to various fields including digital contents management, face recognition, three-dimensional face modeling, animation, avatars, smart surveillance, digital entertainment, and the like. In this regard, face detection technology is becoming increasingly important.
Rapid object detection technology is widely used in fields requiring rapid face detection, for example, 3A (automatic exposure; AE, auto white balance; AWB, automatic focusing; AF) cameras based on face detection, automated teller machines (ATM), digital video recorders (DVR) based on face detection, face recognition phones, face-based photo album classification and face-based photo album retrieval, and the like.
Recently, various studies regarding face detection technology have been carried out. However, detection reliability of an algorithm used in the face detection technology is not sufficient to be applied in real life and a desirable detection speed is not possible. In order to solve the aforementioned problems, research into a method of learning a decision boundary from a face sample pattern and using the decision boundary in face detection is being carried out.
A conventional rapid object detection technology extracts a feature from an input image and consecutively proceeds through stages while discarding an input which does not have an object feature sufficient for a multi-classifier based on a cascade structure. Otherwise, the conventional rapid object detection technology sequentially proceeds through a classification calculation operation in a classifier based on a cascade structure while classifying not only inputs having insufficient object features but also inputs having sufficient object features into an object.
However, the described conventional technologies have to calculate an input image in all stages and have to proceed to a considerably advanced stage even for an input having an insufficient object feature, resulting in a significant amount of unnecessary calculation.